Visual reconstruction
The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A recursive soft-decision approach to blind image deconvolution
IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing
Image statistics and local spatial conditions for nonstationary blurred image reconstruction
Proceedings of the 29th DAGM conference on Pattern recognition
Introducing dynamic prior knowledge to partially-blurred image restoration
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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Blind blur identification in video sequences becomes more important. This paper presents a new method for identifying parameters of different blur kernels and image restoration in a weighted double regularized Bayesian learning approach. A proposed prior solution space includes dominant blur point spread functions as prior candidates for Bayesian estimation. The double cost functions are adjusted in a new alternating minimization approach which successfully computes the convergence for a number of parameters. The discussion of choosing regularization parameters for both image and blur function is also presented. The algorithm is robust in that it can handle images that are formed in variational environments with different types of blur. Numerical tests show that the proposed algorithm works effectively and efficiently in practical applications.